基于多级特征增强融合的红外飞机目标检测方法

doi:10.7527/S1000-6893.2023.29220

Abstract

Abstract:

Infrared aircraft target detection and recognition has broad prospects for applications in the fields of unmanned reconnaissance， target precision guidance， and flight monitoring. To solve the difficulty in accurate recognition caused by few available features and small difference between classes in infrared aircraft target detection and recognition， a Multi-level Feature Enhanced Fusion Network model （MFEFNet） is proposed. Firstly， a Local and Global Feature Enhancement fusion module （LGFE） is designed. By designing the coordinate attention mechanism and the global pixel attention mechanism， the module enhances and merges the deep semantic features and the low-level detail features. The module implements the top-down mode to dynamically guide the low-level local feature enhancement with deep semantics， and then adaptively strengthens the feature map representation of infrared aircraft target information. Then， a Global Extension Module （GEM） is designed. Based on the feature pyramid， the module further aggregates the multi-scale context information of the intermediate feature map to improve the ability of the network to identify multi-class targets. Finally， the GEM module and LGFE module are cascaded to maintain the long-distance dependence of features and produce a joint effect， which further improves the ability to make feature characterization and target classification decisions and effectively solves the problems of missing features and small differences between classes of infrared aircraft targets. In addition， this paper develops a new ground infrared aircraft dataset based on transfer learning， and analyzes the consistency between transfer data and real infrared data at the feature level by using Gram distance. The experimental results on the ground infrared aircraft dataset show that each module of MFEFNet is effective. Compared with other advanced algorithms， the algorithm proposed can improve the mean Average Precision （mAP） of ground infrared aircraft target recognition by more than 4.3%， and has achieved obvious advantages in recognition accuracy.

Key words: infrared aircraft target recognition, machine vision, feature enhancement fusion, global extension fusion, attention mechanism

CLC Number:

V219

Yi ZHANG, Yan ZHANG, Yu ZHANG, Yong ZHANG, Di LIU. Infrared aircraft target detection method based on multi-level feature enhancement fusion[J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(22): 629220.

Figures/Tables 15

Fig.1

Table 1

Structure and parameter settings of feature extraction network

名称	阶段	输出尺寸	骨干结构
ResNet-50	卷积层1	400×512	$7 × 7,64$
	卷积组2	200×256	$1 × 1,64 3 × 3,64 1 × 1,256 × 3$
	卷积组3	100×128	$1 × 1,128 3 × 3,128 1 × 1,512 × 4$
	卷积组4	50×64	$1 × 1,256 3 × 3,256 1 × 1,1 024 × 6$
	卷积组5	25×32	$1 × 1,512 3 × 3,512 1 × 1,2 048 × 3$

Table 1

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Fig.7

Table 2

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Table 4

Fig.8

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Table 5

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提取特征层	与红外图像的格拉姆距离
提取特征层	原始图像	迁移图像
C2	0.246	1.02×10^-4
C3	0.188	1.05×10^-5
C4	0.028	2.40×10^-7
C5	1.22×10^-4	1.21×10^-7

超参数	数值
锚框尺寸	32｛2⁰，2^1/3，2^2/3｝，64｛2⁰，2^1/3，2^2/3｝ 128｛2⁰，2^1/3，2^2/3｝，256｛2⁰，2^1/3，2^2/3｝ 512｛2⁰，2^1/3，2^2/3｝
锚框比例	0.5，1，2
训练集和测试集划分	8：2
优化器	SGD
动量	0.9
权重衰减	0.000 5
初始学习率	0.005
学习率衰减步长	3
学习率衰减系数	0.33
训练轮数	40
批量大小	4

算法	AP	AP^0.5	AP^0.75	AP^s	AP^m	AP^l	帧数	参数量/M
Faster RCNN	0.625	0.787	0.732	0.108	0.697	0.623	17	41.53
Cascade RCNN	0.688	0.819	0.787	0.096	0.727	0.663	15	69.00
YOLOv3	0.612	0.861	0.731	0.103	0.586	0.656	65	3.69
YOLOX	0.655	0.845	0.772	0.086	0.542	0.708	56	8.95
YOLOv8	0.680	0.880	0.810	0.091	0.523	0.731	47	11.15
FCOS	0.546	0.715	0.655	0.297	0.468	0.536	19	31.90
RetinaNet	0.666	0.816	0.784	0.092	0.602	0.702	17	32.95
SSD	0.497	0.641	0.597	0.041	0.401	0.551	93	17.42
MFEFNet	0.731	0.899	0.816	0.258	0.669	0.801	16	43.91

算法	AP	AP^0.5	AP^0.75	AP^s	AP^m	AP^l	帧数	参数量/M
MFEFNet	0.731	0.899	0.816	0.258	0.669	0.801	16	43.91
MFEFNet（w/o GEM）	0.719	0.883	0.798	0.112	0.575	0.768	16	43.91
MFEFNet（w/o LGFE）	0.682	0.822	0.772	0.185	0.633	0.725	17	32.95
MFEFNet（w/o CA）	0.726	0.890	0.837	0.135	0.611	0.761	16	43.78
MFEFNet（w/o GPA）	0.721	0.880	0.847	0.309	0.587	0.758	16	41.28

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Infrared aircraft target detection method based on multi-level feature enhancement fusion

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